Localizing Text in Images and Videos based on Morphology
Mohamed Amin Ben Atitallah1, Rostom Kachouri2, Hassene Mnif3
1Mohamed Amin Ben Atitallah, Laboratory of Electronics and Information Technology (E.N.I.S.), University of Sfax, PHD Student at National Engineering School of Gabes (ENIG), University of Gabes, Tunisia.
2Rostom Kachouri, LIGM, Univ Gustave Eiffel, CNRS, ESIEE Paris, Marne-la-Vallée, France.
3Hassene Mnif, National school of electronics and telecommunications of Sfax, Laboratory of Electronics and Information Technology (E.N.I.S.), University of Sfax, Tunisia.

Manuscript received on April 30, 2020. | Revised Manuscript received on May 06, 2020. | Manuscript published on May 30, 2020. | PP: 1684-1688 | Volume-9 Issue-1, May 2020. | Retrieval Number: A2598059120/2020©BEIESP | DOI: 10.35940/ijrte.A2598.059120
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Many multifaceted images comprise observable text. If the occurrences of this text can be identified, segmented, and recognized automatically, they will be a prized source of high-level semantics; for retrieval and indexing. In this paper, we will propose a novel method for localizing and detecting text in complex images and video frames based on morphology. A morphological Gardient is generated by computing the variance between the dilation and the erosion image. Then the candidate of regions are connected via a morphological closing operation and every text areas are determined used the occurrence of text in each candidate. The identified text regions are localized perfectly via the projection of the text pixels in the morphological Gardient map. This method is sturdy to different position, character size, color and contrast. The updating of the text region between images is also used to minimize the processing time. Tests are realized on divers images to confirm the good efficient of our method. 
Keywords: Object detection, Object segmentation, Morphology Gardient operator, Text detection, Text segmentation, Video processing.
Scope of the Article: Text Detection